Starting from the initial image \(I^{(n)} \in \mathbb {R}^{H_0 \times W_0 \times 3}\) , a conventional CNN backbone (VGG-16 [1]}) generates different levels feature maps \(F_l^{(n)} \in \mathbb {R}^{H_l \times W_l \times C_l}\) . Following [2]}, we connect another side path to the last pooling layer in VGG-16, and only use the last four levels features for the following process. For simplicity, these four features can be denoted as a feature set \(F^{(n)}\) :
\(F^{(n)} = \lbrace F_3^{(n)},F_4^{(n)},F_5^{(n)},F_6^{(n)}\rbrace .\) 
